Real time optimal control and state estimation for Li-ion batteries

Li-Ion batteries are increasingly being looked at as a major alternative to fossil fuels in the transition towards clean energy. This has made created the necessity to be able to understand and predicted their behaviors – with the goal of elongating their life and ensuring safety of use. This thesis investigates the use of optimization-based state estimation and control methods on first-principle, physics-based models for the monitoring and real time control of batteries. Specifically, Moving Horizon Estimation in conjunction with Nonlinear Model Predictive Control applied to the Single Particle and Tank-in-Series Battery Models are investigated. First principle Li-Ion Battery Models consist of a set of coupled differential and algebraic equations. The constants in these equations are battery design parameters, which have been identified for an LGHG2 Cell by referring to relevant literature and conducting parameter estimation using gradient based methods. The two models’ equations are solved using numerical methods after spatial discretization. The optimization problems for state estimation and control are set up and tested offline. The same real time control framework is then deployed onto hardware application with a real battery. The real time control using this setup is tested on a Raspberry Pi, to gauge and optimally charge an LGHG2 3Ah Cell. The battery voltage and current is measured using a TI BQ40Z50 Battery Fuel Gauge, and the charging is done using a TI BQ25700A Buck Boost Charger. The optimization-based state estimation algorithm (MHE) can converge to the measured voltage and recover the model states based on real time current and voltage measurements received from the fuel gauge. The control algorithm (NMPC) can adjust the charging current as the battery nears the voltage setpoint, to prevent overcharging. The designed algorithm can also be easily modified for several objective functions, cell chemistries, and constraints. The novelty in this work is the application onto hardware, and closed loop, real time implementation of a Nonlinear Model Predictive Control algorithm without the use of any lookup tables, where optimization is conducted at each step to find an optimal control action.Mechanical Engineerin